(MSc) Master of Science
Social Data Science
Current
University of Essex
University of Essex
Government
Colchester Campus
Masters
Full-time
Politics and International Relations
MSC L2I112
10/05/2023
Details
Professional accreditation
None
Admission criteria
A 2.2 degree in Political Science, International Relations, International Studies, American Studies, United States Politics, Economics, Finance, Statistics or Political Studies.
OR
A 2.2 degree in any subject which includes study in two relevant modules. Relevant modules include, but are not limited to:
- Comparative Political Systems
- Constitutional Democracy
- Contemporary World Affairs
- Current Affairs
- Democratic Theory
- Econometrics
- European Integration/Dynamics of Integration
- Foreign Policy/Comparative Foreign Policy
- Game Theory
- Governmental Processes/Systems
- Human Rights
- Ideology and Political Analysis
- International Economics Law
- International Economics Relations
- International Trade/Business Law
- International Law
- International Public Relations
- International Security
- Law of Armed Conflict
- Micro/Macro Economics
- Peace Studies
- Philosophy(MA Political Theory only)
- Political Conflict
- Political Decision Making
- Political Economy
- Political Sociology
- Political Studies
- Public Administration
- Public International Law
- Public Policy Analysis
- Quantitative Reasoning
- Security Studies
- Strategic Studies
- Terrorism
- Theories of Development
We will also consider applicants with a degree in an unrelated subject and have at least 5 years' work experience such as working with a NGO.
IELTS (International English Language Testing System) code
IELTS 6.5 overall with a minimum component score of 5.5
If you do not meet our IELTS requirements then you may be able to complete a pre-sessional English pathway that enables you to start your course without retaking IELTS.
Additional Notes
The University uses academic selection criteria to determine an applicant’s ability to successfully complete a course at the University of Essex. Where appropriate, we may ask for specific information relating to previous modules studied or work experience.
Course qualifiers
A course qualifier is a bracketed addition to your course title to denote a specialisation or pathway that you have achieved via the completion of specific modules during your course. The
specific module requirements for each qualifier title are noted below. Eligibility for any selected qualifier will be determined by the department and confirmed by the final year Board of
Examiners. If the required modules are not successfully completed, your course title will remain as described above without any bracketed addition. Selection of a course qualifier is
optional and student can register preferences or opt-out via Online Module Enrolment (eNROL).
None
Rules of assessment
Rules of assessment are the rules, principles and frameworks which the University uses to calculate your course progression and final results.
Additional notes
None
External examiners
Dr Adrian Florea
Senior Lecturer in International Relations
University of Glasgow
External Examiners provide an independent overview of our courses, offering their expertise and help towards our continual improvement of course content, teaching, learning, and assessment.
External Examiners are normally academics from other higher education institutions, but may be from the industry, business or the profession as appropriate for the course.
They comment on how well courses align with national standards, and on how well the teaching, learning and assessment methods allow students to develop and demonstrate the relevant knowledge and skills needed to achieve their awards.
External Examiners who are responsible for awards are key members of Boards of Examiners. These boards make decisions about student progression within their course and about whether students can receive their final award.
Programme aims
- Provide an introduction and solid foundation in data science for students with a social science undergraduate degree.
- Introduce students to big data, new forms of data, and computational methods, such as machine learning and artificial intelligence, to work with such data.
- Teach research design principles within a strong framework of social science applications.
- Familiarize students with analytic methods from both the social sciences and computer sciences.
- Leverage interdisciplinary outlook to endow students with tools for the study of real world problems in a rigorous fashion.
Learning outcomes and learning, teaching and assessment methods
On successful completion of the programme a graduate should demonstrate knowledge and skills as follows:
A: Knowledge and understanding
A1: Advanced knowledge of different modes of explanation and theoretical perspectives in political science.
A2: Understanding of the main quantitative methods used in social data analysis
A3: Critical awareness of the use of evidence in political science.
A4: Knowledge of the main research findings, and main developments and debates in the study of social science data analysis
A5: Systematic knowledge of the relevant sources of information.
Learning methods
A1-5 Lectures, participation in and presentations to seminars and classes, writing essays and dissertation, oral and written feedback on essays.
A1 specifically in GV914 Research Design and the optional modules.
A2 specifically in GV918 Data for Social Data Science and CE888 Data Science and Decision Making and options.
A3 specifically in GV914, and GV993 MA Dissertation in supervision of individual dissertations.
A4 specifically in GV918 and GV914, and optional modules
A5 specifically in GV918, GV914 and CE888.
Assessment methods
A1-A5 through written assignments and essays.
A2 specifically problem sets for data manipulation and coding.
B: Intellectual and cognitive skills
B1: To develop independent thinking
B2: To muster evidence
B3: To evaluate and analyse evidence
B4: To reason critically.
B5: To argue coherently and concisely
B6: To communicate ideas effectively in writing
B7: To carry out independent research
Learning methods
B1-7 participation in and presentations to seminars and classes, individual guidance on researching and writing essays, oral and written feedback on essays.
B3 especially in GV914, CE888/CE880 and GV918
B7 especially in GV993 supervised dissertation.
Assessment methods
B1-B6 written assignments and essays.
B7 through the dissertation.
C: Practical skills
C1: Organise and structure an extended argument
C2: Use concepts correctly
C3: Compile systematic bibliographies.
C4: Provide references according to accepted conventions.
C5: Use libraries and IT to access data, information & scholarly resources
C6: Sift and synthesise complex information
Learning methods
C1-6 participation in and presentations to seminars and classes, individual guidance for essays, individual supervision of dissertations, oral and written feedback on class presentations and essays.
C5 especially in GV918
Assessment methods
C1-6 written assignments and essays, supervised dissertation.
C1 especially in dissertation.
C2-6 specifically in essays and dissertation.
C5 specifically problem sets for data manipulation and coding.
D: Key skills
D1: Clear, focused, relevant and effective expression & communication in English.
D2: Access and organise information from a variety of electronic sources
D3: Apply statistical methods.
D4: To manage projects and timetables. To find, understand and organise information and ideas.
D5: Working with others in pairs and group work.
D6: Positive response to feedback and criticism, ability to work independently
Learning methods
D1-6 participation in and presentations to seminars and classes, written assignments and essays, dissertation.
D2&3 especially in GV918 and CE888/CE880
D5 specifically in group-based in-class work.
D6 specifically in individual guidance on essays, oral and written feedback on essays.
Assessment methods
D1-5 written assignment and essays, in-class presentations, dissertation
D6 trajectory across assignments throughout course of studies, dissertation